Description: With the development of online selling of products, from custom object manufacturing to property business, the digital color picture is a key communication tool. However, since most of these pictures are not color-calibrated, accessing their color information is a difficult problem. Estimating how the colors could be accurately corrected, even simulating what the object would look like under another illuminant, or how the apartment would render with a red wall, would be of considerable benefit to designers as well as customers. Achieving this is still challenging for color image processing because it needs modeling the propagation of light in a complex scene, from only the color information provided by the image. However, we postulate that a full image is actually rich in information provided appropriate color and spectral models are cleverly used.
State-of-the-art methods in color understanding assume scenes to be lit by a single illuminant. This is based on the underlying assumption that taking into account more complicated reflection models, for example by modeling secondary illuminants and inter-reflections of light between objects in the scene, would make the problem even more under-constrained, and therefore unsolvable. During this PhD work, we would like to consider complex reflection phenomena such as multiple illuminants and mutual reflectances, not as a nuisance which complicates color understanding. Instead we are convinced it is a source of additional information which allows enriching the knowledge about the intrinsic properties of the objects in the scene. The aim will be to propose new color formation models in RGB images in order to account such complex phenomena. First, we aim to use this model in the context of multispectral estimation from RGB components. Second, we will face the problem of multiple lightings estimation in non-calibrated RGB images. Finally, we would like to propose new color invariants based on the model designed during the earlier steps. The successful candidate should have a strong background in Computer Vision. Candidates with experience in physics-based computer vision, reflection models and convex optimization are encouraged to apply. The position will be at the Hubert Curien lab in Saint-Etienne (France)(http://laboratoirehubertcurien.fr/), within a dynamic research group of 15 persons working on computer vision. The candidate will be jointly supervised by Damien Muselet(http://perso.univ-st-etienne.fr/muda8804/), Mathieu Hebert, Joost van de Weijer (http://cat.uab.es/~joost/) and Alain Tremeau. A grant will be asked to the Region so that the candidate can spend at least 6 month at the Computer Vision Center (http://www.cvc.uab.es/) in Barcelona with Joost van de Weijer.
Net salary: 1367 euros without teaching activities and 1643 euros with teaching activities (64 hours teaching each year).

Application Instructions: Please send your application to damien.muselet@univ-st-etienne.fr which should include the following documents:
- Letter of intent
- Grades and ranking of your Master's degree
- Scientific CV
- List of publications
- Name of at least two referees